The goal of the proposed research is to achieve predictive monitoring of the acute ketoacidotic state by developing an on-line dynamic clinical model and an algorithm to update it in accord with the actual clinical course by repetitive adaptation of certain model parameters. This model and the adaptation algorithm will ultimately be implemented on a digital computer with an interactive graphic terminal. The model will accept standard clinical measurements and estimate unmeasurable physiological states such as vascular volume, as well as predict the time course of all measurable states. Clinical measurements and proposed therapy would be entered via the terminal, and at random intervals the physician could query the computer for physiological parameters, predicted course, or cumulative past course. The model will contain coupled metabolic and transport loops. The underlying structure will be non-linear, but the adaptive model will actually consist of a sequence of linear dynamic systems with parameters changing from update to update in a stepwise fashion. The metabolic loops will be modelled by aggregated biochemical pathways under control by hormones and metabolites active in the ketotic state. The transport loops will be modelled by compartmental analysis. The overall model will be developed by an interactive and iterative combination of modern systems theory, computer simulation, animal experimentation, and clinical evaluation. We feel that the proposed project is feasible with present day knowledge, has intrinsic clinical significance, and would establish scientific and technical foundations for monitoring of a broad class of acute medical problems.